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SPMP: A JavaScript Support for Shared Persistent Memory on *** 18th

SPMP: A JavaScript Support for Shared Persistent Memory on *...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Zhang, Qipeng Li, Tianyou Deng, Pan Chen, Yuting Huang, Linpeng Rudoff, Andy Shanghai Jiao Tong Univ Shanghai Peoples R China Intel Asia Pacific R&D Co LTD Shanghai Peoples R China Intel Corp Santa Clara CA USA

ISBN: (纸本)9783030050542;9783030050535

JavaScript is widely used for scripting on client side. *** is a JavaScript runtime environment, allowing Javascript to be used for building scalable network applications on server side. However, *** does not support parallel programming, making it difficult to enhance applications' performance. Meanwhile, persistent memory (PM) shows optimistic prospects of being used in server-side applications, while few researches do exist in allowing script languages to support PM-based parallel programming. In this paper, we introduce SPMP, a JavaScript support for shared persistent memory on ***. With SPMP, each process needs to hold PersistentArrayBuffer, an object that is responsible for allocating, managing, and accessing persistent memory. Multiple processes can then share persistent memory and communicate each other by their PersistentArrayBuffer objects. Furthermore, SPMP supports dynamic load-balancing strategies and ensures data coherency, and also supports data persistence in a secondary storage. We have evaluated SPMP against Extended Memory Semantics (EMS, a state-of-the-art model for parallel programming on ***) on two data-intensive tasks. the results show that SPMP is 100 similar to 300x faster than EMS on five basic operations, and 2x faster on complicated parallel computing tasks such as counting words, due to its particular way on memory allocation and mapping.

关键词： Node.js parallel programming Shared persistent memory

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the Impact of Distributed Generation parallel Operation on Smart Grid 5th

The Impact of Distributed Generation Parallel Operation on S...

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5th international conference on Communications, Signal processing, and Systems (CSPS)

作者： Lei, Yufan Han, Guanglin Wang, Yanqun Tianjin Normal Univ Acad Elect & Commun Engn Tianjin 300387 Peoples R China Tianjin Normal Univ Tianjin 300387 Peoples R China

this paper introduces the concept of distributed generation resources and smart grid, and analyzes the impact of distributed generation parallel operation on three aspects of network loss, voltage and relay protection.

ISBN: (纸本)9789811032295;9789811032288

关键词： Smart grid Distributed generation parallel operation Network loss Relay protection

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PLZMA: A parallel Data Compression Method for Cloud Computing 18th

PLZMA: A Parallel Data Compression Method for Cloud Computin...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Wang, Xin Gan, Lin Xu, Jingheng Yang, Jinzhe Xia, Maocai Fu, Haohuan Huang, Xiaomeng Yang, Guangwen Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Key Lab Earth Syst Modeling Minist Educ Beijing Peoples R China Tsinghua Univ Dept Earth Syst Sci Beijing Peoples R China Natl Supercomp Ctr Wuxi Jiangsu Peoples R China Imperial Coll London Dept Comp London England Qingdao Natl Lab Marine Sci & Technol Lab Reg Oceanog & Numer Modeling Qingdao Peoples R China

ISBN: (纸本)9783030050573;9783030050566

Recent decades have seen the rapid development of cloud computing, resulting in a huge breakthrough for people to handle the data produced every second and everywhere. Meanwhile, data compression is becoming increasingly important, due to its great potential in benefiting both the network transportation and the storage. Based on the urgent demand in high-efficient compression method with balanced performance in both merits of compression time and ratio, this paper presents PLZMA, a parallel design of LZMA. Process-level and thread-level parallelisms are implemented according to the algorithm of LZMA, which have gained great improvement in compression time, while ensuring a fair compression ratio. Experimental results on real-world application showed that PLZMA is able to achieve more balanced performance over other famous methods. the parallel design is able to achieve a performance speedup of 8x over the serial baseline, using 12 threads.

关键词： Data compression parallel computing LZMA

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CPU-GPU Collaborated Computation Models for Biological Sequence Alignment with Mirror-Based Work Load Balancing

CPU-GPU Collaborated Computation Models for Biological Seque...

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international conference on parallel and Distributed Systems (ICPADS)

作者： Sanjay Soundarajan Michelle Salomon Jin H. Park Computer Science Department California State University Fresno Fresno CA U.S.A.

ISBN: (纸本)9781728125848

Biological sequence alignment has been used in many application areas of computational biology and bioinformatics and both non-heuristic and heuristic algorithms have been developed and used. Although the high accuracy is guaranteed, non-heuristic approaches, such as Smith-Waterman algorithm, are not popularly used in the real world due to the quadratic time complexity. However, recent technological development on HPC systems made researchers propose a diversified acceleration approaches of the Smith-Waterman algorithm, including GPU based approaches. In this paper, we propose efficient CPU-GPU collaborated computation models for protein sequence alignment based on the Smith-Waterman algorithm to exploit the maximum efficiency on the heterogeneous system with multicore processor(s) and GPU(s). the proposed approach implements the full functionalities of the local sequence alignment, which are BLAST compatible, and uses efficient strategies for work load balancing and exploiting the maximum performance on the heterogeneous system. Our experimental results showed that the best CPU-GPU collaborated computation model outperforms the corresponding serial and the basic GPU computation models with 30.5x and 2.78x speedups, respectively, on the system with two Xeon E5-2670 processors and a Tesla M2075 GPU.

关键词： Graphics processing units Acceleration Computational modeling Multicore processing Biology Heuristic algorithms Load management

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A Big Data processing Framework for Polarity Detection in Social Network Data

A Big Data Processing Framework for Polarity Detection in So...

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： Princy Victor V.P. Lijo School of Computing Science and Engineering VIT University Vellore Tamil Nadu India

Big Data refers to the extremely big datasets that are produced from different areas which exhibits certain trends and associations. Major areas of big data include medical data, sensor data, social networks such as facebook, twitter, youtube etc. Among this, social networks produce large amount of data per millisecond which can be analysed for several predictive and analytic purposes. Tweets produced by twitter is used in sentiment analysis and polarity detection that helps in identifying the attitude, polarity of words, text or documents. Applying polarity detection in big data is a tedious task as it includes both historical and streaming data. Several frameworks have been proposed for analysing both historical and streaming data in big data. In this paper, lambda architecture for polarity detection of tweets has been proposed which analyses both streaming and historical data. Both the data can be analysed in parallel and used for certain predictive and analytic purposes.

关键词： Computer architecture Big Data Communication systems Social networking (online) Sentiment analysis Real-time systems Storms

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Network-Aware Grouping in Distributed Stream processing Systems 18th

Network-Aware Grouping in Distributed Stream Processing Syst...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Chen, Fei Wu, Song Jin, Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Serv Comp Technol & Syst Lab Cluster & Grid Comp Lab Wuhan 430074 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Distributed Stream processing (DSP) systems have recently attracted much attention because of their ability to process huge volumes of real-time stream data with very low latency on clusters of commodity hardware. Existing workload grouping strategies in a DSP system can be classified into four categories (i.e. raw and blind, data skewness, cluster heterogeneity, and dynamic load-aware). However, these traditional stream grouping strategies do not consider network distance between two communicating operators. In fact, the traffic from different network channels makes a significant impact on performance. How to grouping tuples according to network distances to improve performance has been a critical problem. In this paper, we propose a network-aware grouping framework called Squirrel to improve the performance under different network distances. Identifying the network location of two communicating operators, Squirrel sets a weight and priority for each network channel. It introduces Weight Grouping to assign different numbers of tuples to each network channel according to channel's weight and priority. In order to adapt to changes in network conditions, input load, resources and other factors, Squirrel uses Dynamic Weight Control to adjust network channel's weight and priority online by analyzing runtime information. Experimental results prove Squirrel's effectiveness and show that Squirrel can achieve 1.67x improvement in terms of throughput and reduce the latency by 47%.

关键词： Stream processing Load balancing Grouping Network distance

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Online Speech Decoding Optimization Strategy with Viterbi Algorithm on GPU 5

Online Speech Decoding Optimization Strategy with Viterbi Al...

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5th international conference on Advanced Informatics: Concepts theory and Applications, ICAICTA 2018

作者： Arsadjaja, Alfonsus Raditya Imam Kistijantoro, Achmad School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781538648049

Automatic Speech Recognition (ASR) has been popular recently. But the current algorithm for speech recognition is slow and needed the way to recognize faster. One way to achieve it is with GPU, which provides parallel computation;but ASR is hard to parallelize directly. this paper describes how to build parallel ASR system, which requires several steps. First, we must convert the data structure to make it compatible with GPU, then we have to make several kernels that equivalent to the serial algorithm in *** will describe several optimization strategies for make ASR run much faster after we got the correct GPU program. those strategies are based on profiling result and analysis of the GPU program execution flow. Best implementation that we had have a speedup around 5.59-6.18 times from the serial CPU implementation. © 2018 IEEE.

关键词： Graphics processing unit

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Serverless Skeletons for Elastic parallel processing

Serverless Skeletons for Elastic Parallel Processing

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Big Data Intelligence and Computing (DATACOM), international conference on

作者： Stefan Kehrer Jochen Scheffold Wolfgang Blochinger Parallel and Distributed Computing Group Reutlingen University Germany

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Serverless computing is an emerging cloud computing paradigm with the goal of freeing developers from resource management issues. As of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other. these workloads benefit from on-demand and elastic compute resources as well as per-function billing. However, it is still an open research question to which extent parallel applications, which comprise most often complex coordination and communication patterns, can benefit from serverless *** this paper, we introduce serverless skeletons for parallel cloud programming to free developers from both parallelism and resource management issues. In particular, we investigate on the well-known and widely used farm skeleton, which supports the implementation of a wide range of applications. To evaluate our concepts, we present a prototypical development and runtime framework and implement two applications based on our frame-work: Numerical integration and hyperparameter optimization - a commonly applied technique in machine learning. We report on performance measurements for both applications and discuss the usefulness of our approach.

关键词： Measurement Runtime Serverless computing parallel processing Programming Hyperparameter optimization Skeleton

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Performance Evaluation of DBN Learning on Intel Multi- and Manycore architectures 12th

Performance Evaluation of DBN Learning on Intel Multi- and M...

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12th international conference on parallel processing and Applied Mathematics (PPAM)

作者： Olas, Tomasz Mleczko, Wojciech K. Wozniak, Marcin Nowicki, Robert K. Gepner, Pawel Czestochowa Tech Univ Inst Comp & Informat Sci Dabrowskiego 69 PL-42201 Czestochowa Poland Czestochowa Tech Univ Inst Computat Intelligence Armii Krajowej 36 PL-42201 Czestochowa Poland Intel Corp Santa Clara CA USA

ISBN: (纸本)9783319780245;9783319780238

In our previous papers [12,13], we proposed the parallel realization of the Deep Belief Network (DBN). this research confirmed the potential usefulness of the first generation of the Intel MIC architecture for implementing DBN and similar algorithms. In this work, we investigate how the Intel MIC and CPU platforms can be applied to implement efficiently the complete learning process using DBNs with layers corresponding to the Restricted Boltzman Machines. the focus is on the new generation of Intel MIC devices known as Knights Landing. Unlike the previous generation, called Knights Corner, they are delivered not as coprocessors, but as standalone processors. the learning procedure is based on the matrix approach, where learning samples are grouped into packages, and represented as matrices. We study the possible ways of improving the performance taking into account features of the Knights Landing architecture, and parameters of the learning algorithm. In particular, the influence of the package size on the accuracy of learning, as well as on the performance of computations are investigated using conventional CPU and Intel Xeon Phi. the performance advantages of Knights Landing over Knights Corner are presented and discussed.

关键词： Deep Belief Network Restricted Boltzman Machine Multi- and manycore architectures OpenMP Intel Xeon Phi Knights Landing

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Artificial Intelligence Applications and Innovations: 12th IFIP WG 12.5 international conference and Workshops, AIAI 2016, thessaloniki, Greece

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2018年

作者： Lazaros S Iliadis Ilias G Maglogiannis

ISBN: (纸本)9783319831688

this book constitutes the refereed proceedings of the 12th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2016, and three parallel workshops, held in thessaloniki, Greece, in September 2016. the workshops are the third Workshop on New Methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data Workshop, MHDW 2016, and the First Workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE 2016. the 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. the 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. the papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling.

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